基于SWAN模型的波浪能发电站选址研究

孙单勋; 杨智聪; 王卓恒; 綦晓; 邓慧; 欧阳建友

doi:doi:10.19912/j.0254-0096.tynxb.2024-1593

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基于SWAN模型的波浪能发电站选址研究

更新时间：2026-02-06

- 基于SWAN模型的波浪能发电站选址研究
- 基于SWAN模型的波浪能发电站选址研究
- 太阳能学报 2026年47卷第1期页码：730-737
- 作者机构：
- 作者简介：
- 基金信息：
  
  国家自然科学基金青年科学基金（62201226）;广东省基础与应用基础研究基金（2022A1515240021;2021A1515110665）
- DOI：doi:10.19912/j.0254-0096.tynxb.2024-1593
  中图分类号： TM612
- 网络首发：2026-02-05，
  
  纸质出版：2026-02-05
- 稿件说明：
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孙单勋, 杨智聪, 王卓恒, 等. 基于SWAN模型的波浪能发电站选址研究[J]. 太阳能学报, 2026,47(1):730-737.

孙单勋, 杨智聪, 王卓恒, et al. 基于SWAN模型的波浪能发电站选址研究[J]. 2026, 47(1): 730-737.
孙单勋, 杨智聪, 王卓恒, 等. 基于SWAN模型的波浪能发电站选址研究[J]. 太阳能学报, 2026,47(1):730-737. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1593.

孙单勋, 杨智聪, 王卓恒, et al. 基于SWAN模型的波浪能发电站选址研究[J]. 2026, 47(1): 730-737. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1593.

摘要

针对现有波浪能电站选址策略单一的问题

提出一种基于SWAN数值模拟模型和平准化度电成本模型的新型波浪能电站选址策略

并应用研究区域的实际数据验证了该方法的可行性;为了进一步解决波浪能电站并网后可能对电网造成的不稳定性问题

采用BiTCN-BiGRU-Attention组合神经网络对最佳选址点的发电功率进行短期预测

可提高波浪能电站后期运营的稳定性。结果表明

该选址策略可为波浪能电站的合理选址与稳定运行提供科学依据。

Abstract

To address the limitations of traditional siting strategies

a novel siting approach based on the SWAN numerical simulation model and the levelized cost of energy （LCOE） model is proposed. This method is validated using real-world data from the study area. Additionally

to mitigate potential grid instability issues caused by the integration of wave energy power stations

a hybrid neural network model

BiTCN-BiGRU-Attention

is employed to predict the short-term power generation at the optimal site

thereby enhancing the stability of subsequent operations. The results indicate that this siting strategy provides a scientific basis for the rational siting and stable operation of wave energy power stations.

关键词

Keywords

references

邵萌, 史宏达, 赵阳, 等. 基于改进层次分析法的海洋能多能互补选址决策模型[J]. 太阳能学报, 2016, 37(9): 2443-2450.SHAO M, SHI H D, ZHAO Y, et al. Location decision model of hybrid marine energy based on improved AHP method[J]. Acta energiae solaris sinica, 2016, 37(9): 2443-2450.

邵萌, 刘盟楚, 陈玉静, 等. 基于GIS-MCDM-GRU的舟山市波浪能电站选址决策研究[J]. 中国海洋大学学报(自然科学版), 2023, 53(10): 95-106.SHAO M, LIU M C, CHEN Y J, et al. Research on site selection methodology of wave power plant for Zhoushan based on GIS-MCDM-GRU[J]. Periodical of Ocean University of China, 2023, 53(10): 95-106.

许杉敏. 海上风能与波浪能联合发电系统布局优化[D]. 长沙: 中南大学, 2023.XU S M. Layout optimization of co-located offshore windand wave energy generation system[D]. Changsha: Central South University, 2023.

PATEL R P, NAGABABU G, KACHHWAHA S S, et al.Techno-economic analysis of wave energy resource for India[J]. Journal of the Indian Society of Remote Sensing, 2023, 51(2): 371-381.

GAO Q, KHAN S S, SERGIIENKO N, et al.Assessment of wind and wave power characteristic and potential for hybrid exploration in Australia[J]. Renewable and sustainable energy reviews, 2022, 168: 112747.

胡聪, 毛海英, 尤再进, 等. 中国海域波浪能资源分布及波浪能发电装置适用性研究[J]. 海洋科学, 2018, 42(3): 142-148.HU C, MAO H Y, YOU Z J, et al. Study on the distribution of wave energy resources in China and the applicability of wave energy generation device[J]. Marine sciences, 2018, 42(3): 142-148.

BABARIT A, HALS J, MULIAWAN M J, et al.Numerical benchmarking study of a selection of wave energy converters[J]. Renewable energy, 2012, 41: 44-63.

M’ZOUGHI F, LEKUBE J, GARRIDO A J, et al. Machine learning-based diagnosis in wave power plants for cost reduction using real measured experimental data: mutriku wave power plant[J]. Ocean engineering, 2024, 293: 116619.

MOJTAHEDI A, BEYRAMZADEH M, SIADATMOUSA VI S M, et al. The role of different wind input, whitecap dissipation and quadruplet wave-wave interaction terms in wave evolution in Lake Michigan[J]. Ocean engineering, 2024, 307: 118149.

CASTRO-SANTOS L, MARTINS E, GUEDES SOARES C.Economic comparison of technological alternatives to harness offshore wind and wave energies[J]. Energy, 2017, 140: 1121-1130.

王鑫. 波浪能装置阵列布设相关理论方法及其应用研究[D]. 天津: 天津大学, 2019.WANG X. A study on the theory of wave energy converter array layout and its application[D]. Tianjin: Tianjin University, 2019.

LIU J B, SONG D R, LI Q G, et al.Life cycle cost modelling and economic analysis of wind power: a state of art review[J]. Energy conversion and management, 2023, 277: 116628.

O’CONNELL R, KAMIDELIVAND M, FURLONG R, et al. An advanced geospatial assessment of the Levelised cost of energy(LCOE) for wave farms in Irish and western UK waters[J]. Renewable energy, 2024, 221: 119864.

BIAN H H, WANG Q, XU G Z, et al.Research on short-term load forecasting based on accumulated temperature effect and improved temporal convolutional network[J]. Energy reports, 2022, 8: 1482-1491.

卞玉露. 基于BiTCN和预训练的文本情感分析研究[D]. 淮南: 安徽理工大学, 2022.BIAN Y L.Research on Text Emotion Analysis based on BiTCN andPre-training[D]. Huainan: Anhui University of Science & Technology, 2022.

王瑞, 张璐婷, 逯静. 基于新型相似日选取和VMD-NGO-BiGRU的短期光伏功率预测[J]. 湖南大学学报(自然科学版), 2024, 51(2): 68-80.WANG R, ZHANG L T, LU J. Short term photovoltaic power prediction based on new similar day selection and VMD-NGO-BiGRU[J]. Journal of Hunan University (natural sciences), 2024, 51(2): 68-80.

HU M S, ZHENG G Q, SU Z E, et al.Short-term wind power prediction based on improved variational modal decomposition, least absolute shrinkage and selection operator, and BiGRU networks[J]. Energy, 2024, 303: 131951.

黄宇, 顾智勇, 李永玲, 等. 基于时间模式注意力机制的CNN-BiGRU短期负荷预测[J]. 华北电力大学学报(自然科学版), 2023, 50(6): 11-20.HUANG Y, GU Z Y, LI Y L, et al. Short-term power load forecasting based on temporal pattern attention mechanism of CNN-BiGRU[J]. Journal of North China Electric Power University (natural science edition), 2023, 50(6): 11-20.

AHMED A A M, JUI S J J, AL-MUSAYLH M S, et al. Hybrid deep learning model for wave height prediction in Australia’s wave energy region[J]. Applied soft computing, 2024, 150: 111003.

WU H, GAO X Z, HENG J N.Bio-multisensory-inspired gate-attention coordination model for forecasting short-term significant wave height[J]. Energy, 2024, 294: 130887.

武贺, 方舣洲, 张松, 等. 南海岛礁海域波浪能资源分析及总量评估[J]. 太阳能学报, 2022, 43(9): 416-423.WU H, FANG Y Z, ZHANG S, et al. Wave energy characterization and potential estimation for islands of South China Sea[J]. Acta energiae solaris sinica, 2022, 43(9): 416-423.

张君珏, 苏奋振, 王雯玥. 南海资源环境地理研究综述[J]. 地理科学进展, 2018, 37(11): 1443-1453.ZHANG J J, SU F Z, WANG W Y. A review of geographical information research on resources and environment of the South China Sea region[J]. Progress in geography, 2018, 37(11): 1443-1453.

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